In this paper we show how this can be addressed in a case-based reasoning (CBR) context by a metric learning strategy that explicitly considers bias/fairness. Since one of the advantages CBR has over alternative machine learning approaches is interpretability, it is interesting to see how much ...
Yet, the degree of overlap between notions of algorithmic bias and fairness on the one hand, and legal notions of discrimination and equality on the other, is often unclear, leading to misunderstandings between computer science and law... HJP Weerts,R Xenidis,F Tarissan,... - 《Proceedings...
Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-...
Algorithmic bias results in unfair outcomes due to skewed or limited input data, unfair algorithms, or exclusionary practices during AI development.
Algorithmic bias is a fundamental problem when using machine learning and AI to solve problems that involve humans. While monitoring and mitigating algorithmic bias is crucial for the safe and fair utilization of AI, bias measurement becomes an even greater challenge when labels are systematically wron...
The workshop welcomes contributions in all topics related to algorithmic bias and fairness in search and recommendation, focused (but not limited) to: Data Set Collection and Preparation: - Studying the interplay between bias and imbalanced data. ...
Potentialuse cases for AI in healthcarecontinue to grow as the technology rapidly advances. However, the potential for AI to enhance clinical decision support, chronic disease management and population health efforts has been checked by concerns over pitfalls like model bias and fairness...
Considerations for addressing bias in artificial intelligence for health equity Article Open access 12 September 2023 References Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Conf. on Fairness, Accountability and Transparency 77–...
Fairness and bias correction in machine learning for depression prediction across four study populations Vien Ngoc Dang Anna Cascarano Karim Lekadir Scientific Reports(2024) Machine learning for data-centric epidemic forecasting Alexander Rodríguez
A Survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54(6):1-35. doi:10.1145/3457607 Google ScholarCrossref 18. Chen J, Kallus N, Mao X, Svacha G, Udell M. Fairness Under Unawareness: Assessing Disparity When Protected Cl...